Analyzing Film Censorship in India: CBFC Watch

1 Introduction

This notebook contains the data analysis for the charts and numbers used on our site, CBFC.WATCH. To ensure full transparency with our readers, we are publishing our methodology and the R code used to generate the statistics for various parts of the site, as well as to provide a starting point for others who may be curious about how to use the data.

We explore these questions:

1. Do some regional CBFC offices require more modifications than others?
2. How have the primary reasons for censorship (e.g., violence, profanity, sexual content) trended over time?
3. How much footage is typically removed from a film?
4. Which specific censorship categories are becoming more frequent, and which are declining?
5. What specific words and themes are most associated with each major censorship category?
6. Which specific films have had the most footage removed by each regional office?

1.1 Data Loading and Preparation

Importing the dataset, correcting data types, and standardizing categorical information like language and regional office names.

data <- read_csv("https://github.com/diagram-chasing/censor-board-cuts/raw/refs/heads/master/data/data.csv", 
                 col_types = cols(.default = "c")) %>%
  mutate(
    cert_date = as.Date(cert_date),
    total_modified_time_secs = as.numeric(total_modified_time_secs),
    deleted_secs = as.numeric(deleted_secs),
    replaced_secs = as.numeric(replaced_secs),
    inserted_secs = as.numeric(inserted_secs)
  ) %>%
  # The 'certifier' column contains a string like "Examining Committee, Mumbai".
  # We extract the regional office name by splitting the string by the comma
  # and taking the last element.
  mutate(
    office = str_split(certifier, ",") %>%
      map_chr(last) %>%
      str_trim()
  ) %>%
  separate_rows(ai_content_types, sep = "\\|") %>%
  mutate(ai_content_types = str_trim(ai_content_types)) %>%
  # Filter out any rows where the content type is empty after separation.
  filter(ai_content_types != "") %>%
  # Standardize language names to correct for typos and variations in the raw data
  mutate(
    language = case_when(
      language == "Oriya" ~ "Odia",
      language == "Gujrati" ~ "Gujarati",
      language == "Chhatisgarhi" ~ "Chhattisgarhi",
      language == "Hariyanvi" ~ "Haryanvi",
      language == "Hindi Dub" ~ "Hindi Dubbed",
      TRUE ~ language
    )
  ) %>%
  filter(!is.na(language))

1.2 Exploratory Data Analysis

1.2.1 Languages in the Dataset

Counting how many films for each language in the dataset. We’ll show the top ones.

films_by_language_data <- data %>%
  distinct(id, language) %>%
  count(language, sort = TRUE) %>%
  top_n(10, n) %>%
  mutate(language = fct_reorder(language, n))

# write_json(films_by_language_data, "films_by_language.json", pretty = TRUE, auto_unbox = TRUE)

films_by_language_data %>%
  ggplot(aes(x = n, y = language)) +
  geom_col(fill = tertiary_color, alpha = 0.9) +
  geom_text(aes(label = comma(n)), hjust = -0.15, size = 3.5, color = "gray20") +
  scale_x_continuous(
    labels = comma,
    expand = expansion(mult = c(0, 0.12))
  ) +
  labs(
    title = "Top 15 Languages by Number of Films Censored",
    subtitle = "Hindi, Telugu, and Tamil are the languages with the most films",
    x = "Number of Unique Films",
    y = NULL
  ) +
  theme_cbfc()

1.2.2 Reasons for Modification

The most common reasons for modifications, based on the AI-classified content types.

mods_by_content_data <- data %>%
  filter(!is.na(ai_content_types)) %>%
  count(ai_content_types, sort = TRUE) %>%
  top_n(15, n) %>%
  mutate(
    pretty_name = str_replace_all(ai_content_types, "_", " ") %>% str_to_title(),
    pretty_name = fct_reorder(pretty_name, n)
  )

write_json(mods_by_content_data, "modifications_by_content.json", pretty = TRUE, auto_unbox = TRUE)

mods_by_content_data %>%
  ggplot(aes(x = n, y = pretty_name)) +
  geom_col(fill = secondary_color, alpha = 0.9) +
  geom_text(aes(label = comma(n)), hjust = -0.15, size = 3, color = "gray20") +
  scale_x_continuous(
    labels = comma,
    expand = expansion(mult = c(0, 0.1))
  ) +
  labs(
    title = "Most Common Reasons for Film Modifications",
    x = "Number of Modifications",
    y = "Content Category"
  ) +
  theme_cbfc()

1.2.3 Duration of Edits by Action

We’ve classified each modification log into verbs depending on what action was taken. For example, adding a smoking disclaimer might be an ‘Insertion’ but removing an entire scene is ‘Deletion’. There are also replacements, audio modifications, visual modifications (such as blurs), and so on. We can look at the general distribution for each of these edits.

duration_summary <- data %>%
  filter(total_modified_time_secs > 0, !is.na(ai_action)) %>%
  group_by(ai_action) %>%
  summarise(
    min = min(total_modified_time_secs, na.rm = TRUE),
    q1 = quantile(total_modified_time_secs, 0.25, na.rm = TRUE),
    median = median(total_modified_time_secs, na.rm = TRUE),
    q3 = quantile(total_modified_time_secs, 0.75, na.rm = TRUE),
    max = max(total_modified_time_secs, na.rm = TRUE),
    count = n(),
    .groups = 'drop'
  ) %>%
  mutate(pretty_name = str_replace_all(ai_action, "_", " ") %>% str_to_title()) %>%
  arrange(median)


# Export sample of raw data for BoxX component
duration_boxplot_data <- data %>%
   filter(
    total_modified_time_secs > 0, 
    total_modified_time_secs < 1400, # Found that anything above this is mostly incorrectly entered data
    !is.na(ai_action),
    ai_action != 'content_overlay',
    movie_name != 'ARISHADVARGA' # this movie clearly has a data processing error where the times are added cumulatively https://archive.org/details/cbfc-ecinepramaan-100020292100000261
  ) %>%
  mutate(pretty_name = str_replace_all(ai_action, "_", " ") %>% str_to_title()) %>%
  group_by(ai_action, pretty_name) %>%
  # Sample up to 500 points. If a group has < 500 points, it will take all of them.
  slice_sample(n = 500) %>%
  ungroup() %>%
  select(
    action = ai_action,
    category = pretty_name, 
    duration = total_modified_time_secs
  )


write_json(duration_summary, "duration_by_action_summary.json", pretty = TRUE, auto_unbox = TRUE)
write_json(duration_boxplot_data, "duration_by_action_boxplot.json", pretty = TRUE, auto_unbox = TRUE)

  duration_boxplot_data %>% 
  ggplot(aes(
    x = fct_reorder(category, duration, .fun = median),
    y = duration,
    fill = category
  )) +
  geom_boxplot(show.legend = FALSE, alpha = 0.8, fill = wes_colors[3]) +
  scale_y_log10(
    breaks = c(1, 10, 60, 300, 1800),
    labels = c("1 sec", "10 secs", "1 min", "5 mins", "30 mins")
  ) +
  coord_flip() +
  labs(
    title = "How Long are Different Types of Edits?",
    subtitle = "Distribution of modification durations for each action type on a logarithmic scale.",
    x = NULL,
    y = "Duration of Modification"
  ) +
  theme_cbfc()

1.3 Modification Reasons by Movie Rating

The dataset includes tags for what reason a particular modification was made; violence, profanity and so on. While I have a pretty good idea of what this will give, we can see what is the breakdown of each reason by the rating for that movie (rating is the U, UA, A classification for who can watch the movie).

rating_breakdown <- data %>%
  filter(rating %in% c("U", "UA", "A"), !is.na(ai_content_types)) %>%
  mutate(content_category = case_when(
    ai_content_types %in% c("sexual_explicit", "sexual_suggestive") ~ "Sexual Content",
    ai_content_types == "profanity" ~ "Profanity",
    ai_content_types == "violence" ~ "Violence",
    ai_content_types == "substance" ~ "Substance Use",
    ai_content_types == "religious" ~ "Religious Content",
    ai_content_types == "political" ~ "Political Content",
    TRUE ~ "Other"
  )) %>%
  count(rating, content_category, name = "modification_count", sort = TRUE) %>%
  group_by(rating) %>%
  mutate(percentage = modification_count / sum(modification_count)) %>%
  ungroup()

common_types <- rating_breakdown %>%
  filter(content_category != "Other") %>%
  group_by(content_category) %>%
  summarise(total = sum(modification_count), .groups = 'drop') %>%
  pull(content_category)

rating_breakdown %>%
  filter(content_category %in% common_types) %>%
  ggplot(aes(
    x = percentage,
    y = fct_reorder(content_category, percentage, .desc = FALSE),
    fill = rating
  )) +
  geom_col(color = "white", linewidth = 0.5) +
  facet_wrap(~rating, scales = "free_y", ncol = 3) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_fill_manual(
    values = c("U" = wes_colors[1], "UA" = wes_colors[2], "A" = wes_colors[3])
  ) +
  labs(
    title = "Censorship reasons by film rating",
    subtitle = "Proportional breakdown of modification reasons within each film rating category.",
    x = "% of all modifications for this rating",
    y = NULL
  ) +
  theme_cbfc() +
  theme(
    strip.text = element_text(size = 14, face = "bold", color = "gray20", margin = margin(t = 5, b = 10)),
    strip.background = element_blank(),
    panel.grid.major.x = element_line(color = "gray90", linetype = "dotted"),
    panel.spacing.x = unit(2, "lines"),
    legend.position = "none"
  )

write_json(rating_breakdown, "rating_breakdown.json", pretty = TRUE, auto_unbox = TRUE)

1.4 Average Cut Time by Regional Office

Different regional offices of the CBFC may apply censorship standards differently. Here, we calculate the average total time modified per film for each major office. We filter out extreme outliers (films with more than 30 minutes of cuts) and only include offices that have certified a sufficient number of films (at least 50).

film_summary <- data %>%
  filter(!is.na(office) & !str_detect(office, "\\.mp4$")) %>%
  group_by(id, movie_name, office) %>%
  summarise(
    total_secs = sum(total_modified_time_secs, na.rm = TRUE),
    .groups = 'drop'
  ) %>%
  filter(total_secs > 0, total_secs < 1800)

office_stats <- film_summary %>%
  group_by(office) %>%
  filter(n() >= 50) %>%
  summarise(
    film_count = n(),
    mean_total_secs = mean(total_secs, na.rm = TRUE),
    se = sd(total_secs, na.rm = TRUE) / sqrt(n()),
    ci_lower_secs = pmax(0, mean_total_secs - 1.96 * se),
    ci_upper_secs = mean_total_secs + 1.96 * se,
    .groups = 'drop'
  ) %>%
  arrange(desc(mean_total_secs))

write_json(office_stats, "cuts_by_office.json", pretty = TRUE, auto_unbox = TRUE)

summary_table <- office_stats %>%
  mutate(
    `Average Time` = format_seconds(mean_total_secs),
    `95% CI Lower` = format_seconds(ci_lower_secs),
    `95% CI Upper` = format_seconds(ci_upper_secs)
  ) %>%
  select(
    Office = office,
    `Films Analyzed` = film_count,
    `Average Time`,
    `95% CI Lower`,
    `95% CI Upper`
  )

summary_table %>%
  gt() %>%
  tab_header(
    title = "Average Modification Time per Film by CBFC Regional Office",
    subtitle = "Analysis of offices with at least 50 certified films"
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = wes_colors[1], alpha = 0.3),
      cell_text(weight = "bold")
    ),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_body(columns = c(`Films Analyzed`, `Average Time`, `95% CI Lower`, `95% CI Upper`))
  ) %>%
  cols_align(
    align = "left",
    columns = Office
  ) %>%
  tab_options(
    table.font.names = "Atkinson Hyperlegible",
    heading.title.font.size = 16,
    heading.subtitle.font.size = 12,
    column_labels.font.size = 12,
    table.font.size = 11
  )

Average Modification Time per Film by CBFC Regional Office
Analysis of offices with at least 50 certified films
Office	Films Analyzed	Average Time	95% CI Lower	95% CI Upper
Chennai	1458	2m 46s	2m 33s	2m 59s
Thiruvananthpuram	607	2m 45s	2m 26s	3m 04s
Delhi	309	2m 20s	1m 57s	2m 43s
Mumbai	4192	2m 17s	2m 11s	2m 22s
Bangalore	773	1m 36s	1m 24s	1m 47s
Kolkata	133	1m 35s	1m 14s	1m 56s
Hyderabad	849	1m 34s	1m 24s	1m 45s

1.5 Trends in Censorship Content Types

Have the reasons for film censorship changed over time? This analysis tracks the changes in different modification types (Violence, Profanity, Sexual Content, etc.) on a monthly basis.

For each category, we calculate its deviation from its own historical average (the “baseline”). This method clearly shows periods when a certain type of content was censored more or less frequently than usual. We also apply a 3-month moving average to smooth out short-term noise and reveal the underlying trend.

interpolate_at_zero <- function(df) {
  sign_change <- which(diff(sign(df$deviation_pct_smooth)) != 0)
  if (length(sign_change) == 0) return(df)
  
  interpolated <- map_df(sign_change, ~{
    slice <- df[.x:(.x + 1), ]
    zero_cross_date <- approx(slice$deviation_pct_smooth, as.numeric(slice$year_month), xout = 0)$y
    if (is.na(zero_cross_date)) return(tibble())
    tibble(
      year_month = as.Date(zero_cross_date, origin = "1970-01-01"),
      deviation_pct_smooth = 0,
      above_baseline = slice$above_baseline[2],
      content_type = slice$content_type[1],
      baseline_rate = slice$baseline_rate[1]
    )
  })
  
  bind_rows(df, interpolated) %>% arrange(year_month)
}

chart_data <- data %>%
  mutate(
    year_month = floor_date(cert_date, "month"),
    content_type = case_when(
      ai_content_types %in% c("sexual_explicit", "sexual_suggestive") ~ "Sexual Content",
      ai_content_types == "profanity" ~ "Profanity",
      ai_content_types == "violence" ~ "Violence",
      ai_content_types == "substance" ~ "Substance Use",
      ai_content_types == "religious" ~ "Religious Content",
      ai_content_types == "political" ~ "Political Content",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(content_type)) %>%
  group_by(year_month, content_type) %>%
  summarise(films_with_mods = n_distinct(certificate_id), .groups = "drop") %>%
  left_join(
    data %>%
      mutate(year_month = floor_date(cert_date, "month")) %>%
      group_by(year_month) %>%
      summarise(total_films = n_distinct(certificate_id)),
    by = "year_month"
  ) %>%
  mutate(rate = films_with_mods / total_films) %>%
  filter(total_films >= 10) %>%
  group_by(content_type) %>%
  arrange(year_month) %>%
  mutate(
    baseline_rate = mean(rate),
    deviation_pct = (rate - baseline_rate) / baseline_rate * 100,
    deviation_pct_smooth = zoo::rollmean(deviation_pct, k = 3, fill = NA, align = "center"),
    above_baseline = ifelse(is.na(deviation_pct_smooth), NA, deviation_pct_smooth > 0)
  ) %>%
  ungroup() %>%
  filter(!is.na(deviation_pct_smooth)) %>%
  mutate(content_type = fct_reorder(content_type, baseline_rate, .desc = TRUE)) %>%
  group_by(content_type) %>%
  group_modify(~ interpolate_at_zero(.x)) %>%
  ungroup()

ggplot(chart_data, aes(x = year_month, y = deviation_pct_smooth, color = above_baseline, group = 1)) +
  geom_hline(yintercept = 0, color = "grey40", linetype = "dashed") +
  geom_line(linewidth = 1.2) +
  facet_wrap(~content_type, ncol = 3) +
  scale_color_manual(
    values = c("TRUE" = wes_colors[5], "FALSE" = wes_colors[1]),
    labels = c("Below Baseline", "Above Baseline"),
    name = NULL
  ) +
  scale_x_date(
    date_labels = "'%y",
    date_breaks = "2 years",
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(ifelse(x >= 0, "+", ""), round(x, 0), "%")
  ) +
  labs(
    title = "Censorship Patterns vs. Historical Average",
    caption = "Note: Dashed line is the baseline average for that category. Red indicates months with above-average censorship rates; blue is below-average.\nA 3-month moving average has been applied to smooth trends. Source: Central Board of Film Certification.",
    x = NULL,
    y = "Deviation from Baseline"
  ) +
  theme_cbfc() +
  theme(
    plot.caption = element_text(size = 9, color = "grey50", hjust = 0, margin = margin(t = 16)),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.4),
    legend.position = "bottom"
  )

write_json(chart_data, "content_trends.json", pretty = TRUE, auto_unbox = TRUE)

1.6 Distribution of Modification Durations

This histogram shows the distribution of total modification times for films that had at least one cut. Approximately half of the films in the dataset have ‘zero seconds’ of edits (meaning modifications might have been made but they were not logged with a duration of edit); this chart focuses only on those that have duration values.

film_data <- data %>%
  group_by(id) %>%
  summarise(
    total_modified_time_secs = sum(total_modified_time_secs, na.rm = TRUE),
    .groups = 'drop'
  )

total_films <- nrow(film_data)
percent_zero <- mean(film_data$total_modified_time_secs == 0, na.rm = TRUE)
film_data_positive <- film_data %>% filter(total_modified_time_secs > 0)
median_val_positive <- median(film_data_positive$total_modified_time_secs)

p_dist <- ggplot(film_data_positive, aes(x = total_modified_time_secs)) +
  geom_histogram(
    aes(y = after_stat(count) / total_films * 100),
    fill = wes_colors[2],
    bins = 40,
    boundary = 0
  ) +
  geom_vline(
    xintercept = median_val_positive,
    linetype = "dashed",
    color = "gray20",
    linewidth = 0.8
  ) +
  # Use a log10 scale on the x-axis because the data is highly skewed.
  scale_x_log10(
    breaks = c(1, 10, 60, 300, 1800),
    labels = c("1s", "10s", "1m", "5m", "30m")
  ) +
  scale_y_continuous(
    expand = expansion(mult = c(0, 0.05)),
    labels = percent_format(scale = 1)
  ) +
  labs(
    title = "Distribution of Modification Times",
    subtitle = str_glue("Comparing total modification time for films with at least one cut.
                        This chart excludes the {percent(percent_zero, accuracy=1)} of films with zero modifications."),
    x = "Total Modification Time (Logarithmic Scale)",
    y = "Percent of All Films"
  ) +
  theme_cbfc() +
  theme(
    panel.grid.major.x = element_blank()
  )

print(p_dist)

# Export the data used for the histogram to a JSON file.
# We use ggplot_build() to extract the computed data from the plot object.
hist_data_for_export <- ggplot_build(p_dist)$data[[1]] %>%
  select(x_min = xmin, x_max = xmax, count, y_percent_total = y)

export_data_dist <- list(
  histogram_bins = hist_data_for_export,
  statistics = list(
    median_positive_secs = median_val_positive,
    total_films = total_films,
    percent_zero_mods = percent_zero
  ),
  axis_config = list(
    breaks = c(1, 10, 60, 300, 1800),
    labels = c("1s", "10s", "1m", "5m", "30m")
  )
)

write_json(export_data_dist, "histogram_data.json", pretty = TRUE, auto_unbox = TRUE)

1.7 Identifying Trending Censorship Categories

To determine which censorship categories are increasing or decreasing over time, we use a logistic regression model.

A positive slope from the model indicates an increasing trend, while a negative slope indicates a decreasing trend. We then visualize the top three fastest-increasing and fastest-decreasing categories since 2018.

This is based on Julia Silge’s excellent article demonstrating a similar type of analysis.

categories_by_quarter <- data %>%
  filter(!is.na(cert_date), !is.na(ai_content_types), cert_date >= as.Date("2018-01-01")) %>%
  mutate(quarter = floor_date(cert_date, unit = "3 months")) %>%
  count(quarter, ai_content_types, name = "category_count") %>%
  group_by(quarter) %>%
  mutate(quarter_total_cuts = sum(category_count)) %>%
  ungroup() %>%
  group_by(ai_content_types) %>%
  filter(sum(category_count) > 100) %>%
  ungroup()

category_models <- categories_by_quarter %>%
  nest(data = -ai_content_types) %>%
  # (total - category) as a function of time (quarter).
  mutate(
    model = map(data, ~ glm(
      cbind(category_count, quarter_total_cuts - category_count) ~ quarter,
      data = .,
      family = "binomial"
    ))
  )

slopes <- category_models %>%
  mutate(tidied = map(model, tidy)) %>%
  unnest(tidied) %>%
  filter(term == "quarter") %>%
  arrange(desc(estimate))

trends_to_plot <- bind_rows(
  slopes %>% top_n(3, estimate),
  slopes %>% top_n(-3, estimate)
)

ggplot(
  categories_by_quarter %>% inner_join(trends_to_plot, by = "ai_content_types"),
  aes(x = quarter, y = category_count / quarter_total_cuts, 
      color = fct_reorder2(ai_content_types, quarter, category_count))
) +
  geom_line(alpha = 0.8, linewidth = 1.2) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed", linewidth = 0.5) +
  facet_wrap(~ ifelse(estimate > 0, "Increasing Trends", "Decreasing Trends"), scales = "free_y") +
  scale_y_continuous(labels = percent_format()) +
  scale_color_manual(values = wes_colors) +
  labs(
    title = "Which Censorship Categories Have Trended Up or Down?",
    subtitle = "Proportion of all modifications per quarter since 2018, with a linear trendline.",
    x = "Date of Certification",
    y = "Percent of All Cuts in Quarter",
    color = "Content Type"
  ) +
  theme_cbfc() +
  theme(legend.position = "bottom")

# Export trending categories data
# Create clean version of trends_to_plot without model objects
trends_clean <- trends_to_plot %>% 
  select(ai_content_types, estimate, std.error, statistic, p.value)

trending_export <- list(
  slopes = slopes %>% select(ai_content_types, estimate, std.error, statistic, p.value),
  quarterly_data = categories_by_quarter %>%
    inner_join(trends_clean, by = "ai_content_types") %>%
    select(quarter, ai_content_types, category_count, quarter_total_cuts, 
           estimate, std.error, statistic, p.value)
)
write_json(trending_export, "trending_categories.json", pretty = TRUE, auto_unbox = TRUE)

1.8 Keyword Co-occurrence Network

This analysis explores which terms tend to appear together within the same film’s modification records.

We calculate the pairwise correlation between the 50 most common keywords and visualize the results as a network graph.

This contains a lot of NSFW stuff, but hiding it would…probably defeat the point.

reference_words <- data %>%
  filter(!is.na(ai_reference)) %>%
  mutate(word = str_split(tolower(ai_reference), "\\|")) %>%
  unnest(word) %>%
  filter(!is.na(word), !str_detect(word, "violence|scene|visual|dialogue")) %>%
  select(id, word)

word_counts <- reference_words %>%
  count(word, sort = TRUE)

word_pairs <- reference_words %>%
  filter(word %in% (word_counts %>% top_n(50, n) %>% pull(word))) %>%
  pairwise_cor(item = word, feature = id, sort = TRUE)

word_pairs %>%
  filter(correlation > 0.05) %>%
  graph_from_data_frame() %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(edge_alpha = correlation), show.legend = FALSE) +
  geom_node_point(color = wes_colors[3], size = 5) +
  geom_node_text(aes(label = name), repel = TRUE) +
  theme_void() +
  labs(
    title = "Which Censorship Terms Appear Together?",
    subtitle = "Co-occurrence network of the 50 most common keywords in CBFC modification records."
  )

# Export co-occurrence network data
network_export <- list(
  word_counts = word_counts %>% top_n(50, n),
  word_pairs = word_pairs %>% filter(correlation > 0.05)
)
write_json(network_export, "keyword_network.json", pretty = TRUE, auto_unbox = TRUE)

1.9 Most Important Keywords by Category (TF-IDF)

While the network graph shows which words co-occur, it doesn’t tell us which words are most characteristic of a specific censorship category. For that, we use a metric called Term Frequency-Inverse Document Frequency (TF-IDF).

TF-IDF identifies words that are common within one category (e.g., “blood” in the “violence” category) but are relatively rare in all other categories.

reference_tokens <- data %>%
  filter(!is.na(ai_reference), !is.na(ai_content_types)) %>%
  select(ai_content_types, ai_reference) %>%
  mutate(word = str_split(tolower(ai_reference), "\\|")) %>%
  unnest(word) %>%
  filter(word != "")

reference_tfidf <- reference_tokens %>%
  count(ai_content_types, word, sort = TRUE) %>%
  bind_tf_idf(term = word, document = ai_content_types, n = n) %>%
  arrange(desc(tf_idf))

top_terms_table <- reference_tfidf %>%
  group_by(ai_content_types) %>%
  slice_max(order_by = tf_idf, n = 5) %>%
  ungroup() %>%
  select(Category = ai_content_types, Term = word, `TF-IDF` = tf_idf) %>%
  filter(Category %in% c("violence", "profanity", "sexual_suggestive", "substance", "political", "religious"))

top_terms_table %>%
  gt() %>%
  tab_header(
    title = "Most Distinctive Keywords by Censorship Category",
    subtitle = "Using Term Frequency-Inverse Document Frequency (TF-IDF) analysis"
  ) %>%
  fmt_number(
    columns = `TF-IDF`,
    decimals = 3
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = wes_colors[2], alpha = 0.3),
      cell_text(weight = "bold")
    ),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(transform = "capitalize"),
    locations = cells_body(columns = Category)
  ) %>%
  tab_style(
    style = cell_text(style = "italic"),
    locations = cells_body(columns = Term)
  ) %>%
  cols_align(
    align = "center",
    columns = `TF-IDF`
  ) %>%
  tab_options(
    table.font.names = "Atkinson Hyperlegible",
    heading.title.font.size = 16,
    heading.subtitle.font.size = 12,
    column_labels.font.size = 12,
    table.font.size = 11,
    row_group.font.weight = "bold"
  )

Most Distinctive Keywords by Censorship Category
Using Term Frequency-Inverse Document Frequency (TF-IDF) analysis
Category	Term	TF-IDF
political	national flag	0.020
political	modi	0.015
political	indian flag	0.009
political	pakistan	0.007
political	political leaders	0.007
profanity	munda	0.005
profanity	bhenchod	0.004
profanity	iththa	0.003
profanity	bhadkov	0.003
profanity	maal	0.003
religious	superstition	0.044
religious	black magic	0.015
religious	religion	0.014
religious	superstitions	0.012
religious	religious sentiments	0.011
sexual_suggestive	kissing scene	0.022
sexual_suggestive	cleavage	0.019
sexual_suggestive	sexual_suggestive	0.018
sexual_suggestive	kissing	0.017
sexual_suggestive	love making scene	0.017
substance	tobacco	0.028
substance	liquor label	0.017
substance	akshay kumar	0.016
substance	liquor labels	0.015
substance	rahul dravid	0.014
violence	blood	0.032
violence	bloodshed	0.009
violence	killing scene	0.007
violence	dead bodies	0.007
violence	dead body	0.006

write_json(reference_tfidf %>% slice_head(n = 300), "tfidf_analysis.json", pretty = TRUE, auto_unbox = TRUE)

1.10 Top Censored Films by Office

Identifies the top films that had the most time removed by each major CBFC regional office.

film_level_summary <- data %>%
  filter(!is.na(office) & !str_detect(office, "\\.mp4$")) %>%
  group_by(id, movie_name, office, language, cert_date, cert_no) %>%
  summarise(
    total_cuts = n(),
    total_time_removed_secs = sum(total_modified_time_secs, na.rm = TRUE),
    .groups = 'drop'
  ) %>%
  filter(total_time_removed_secs > 0, total_time_removed_secs < 1800) %>%
  mutate(
    year = map2_dbl(cert_date, cert_no, ~ extract_year(.x, .y)),
    cleaned_name = map_chr(movie_name, clean_name),
    slug = map2_chr(cleaned_name, year, make_slug)
  )

top_censored_by_office <- film_level_summary %>%
  group_by(office) %>%
  arrange(desc(total_time_removed_secs)) %>%
  slice_head(n = 10) %>%
  ungroup() %>%
  mutate(
    duration_formatted = format_seconds(total_time_removed_secs)
  ) %>%
  arrange(office, desc(total_time_removed_secs)) %>%
  select(Office = office, Language = language, `Film Name` = cleaned_name, 
         `Time Removed` = duration_formatted, `Total Cuts` = total_cuts, Slug = slug)

display_table <- top_censored_by_office %>%
  select(-Slug)

display_table %>%
  gt() %>%
  tab_header(
    title = "Top 10 Most Censored Films by Time Removed",
    subtitle = "Films with the highest modification times for each regional office"
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = wes_colors[3], alpha = 0.3),
      cell_text(weight = "bold")
    ),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(style = "italic"),
    locations = cells_body(columns = `Film Name`)
  ) %>%
  cols_align(
    align = "center",
    columns = c(Language, `Time Removed`, `Total Cuts`)
  ) %>%
  cols_align(
    align = "left",
    columns = c(Office, `Film Name`)
  ) %>%
  tab_options(
    table.font.names = "Atkinson Hyperlegible",
    heading.title.font.size = 16,
    heading.subtitle.font.size = 12,
    column_labels.font.size = 12,
    table.font.size = 11
  ) %>%
  tab_style(
    style = cell_borders(
      sides = c("top", "bottom"),
      color = "lightgray",
      weight = px(1)
    ),
    locations = cells_body()
  )

Top 10 Most Censored Films by Time Removed
Films with the highest modification times for each regional office
Office	Language	Film Name	Time Removed	Total Cuts
Bangalore	Kannada	NEGILA ODEYA	26m 22s	3
Bangalore	Hindi	REHNA NAHI BIN TERE	23m 02s	1
Bangalore	Kannada	TIGER GALLI	19m 45s	128
Bangalore	Kannada	THUGS OF RAMAGHADA	19m 42s	19
Bangalore	Kannada	M E S T R I (REVISED)	18m 20s	21
Bangalore	Kannada	YAAR YAARO GHORIMELE	15m 09s	58
Bangalore	Kannada	MARKATA	15m 08s	6
Bangalore	Kannada	ROYAL MECH	14m 22s	2
Bangalore	Kannada	BENGALURU UNDERWORLD	12m 18s	75
Bangalore	Kannada	YOGI DUNIYA (REVISED)	11m 19s	28
Chennai	Tamil	IDHU ENGA BHOOMI (RE-REVISED)	27m 32s	33
Chennai	Telugu	PULIDEBBA (REVISED)	24m 44s	17
Chennai	Tamil	'AALAPIRANTHAVAN' (REVISED)	24m 22s	25
Chennai	Tamil	NERAM NALLA NERAM (REVISED)	24m 07s	23
Chennai	Tamil	NAAN UNNA NINACHEN (REVISED)	23m 35s	25
Chennai	Tamil	THEEVIRAM	22m 43s	6
Chennai	Telugu	DEBBAKU DEBBA (REVISED)	22m 07s	12
Chennai	Telugu	DARJA DONGA (REVISED)	22m 05s	15
Chennai	Telugu	BANDIPOTU SIMHAM (REVISED)	21m 47s	21
Chennai	Telugu	KONDAVEETI SIVA (REVISED)	21m 05s	23
Cuttack	Odia	TU MO SWEET SIXTEEN	8m 00s	3
Cuttack	Hindi	" YEH KAISA TIGDAM "	6m 50s	7
Cuttack	Odia	PADIGALI TO PREMARE	5m 57s	9
Cuttack	Bhojpuri	V I P 2	4m 58s	1
Cuttack	Chhattisgarhi	SUPER HERO BHAISHA	4m 40s	4
Cuttack	Bhojpuri	IDDARAMMAYILATHO	4m 12s	1
Cuttack	Odia	SUPER RAKSHAK	4m 00s	2
Cuttack	Hindi	CHOR CHOR	3m 31s	13
Cuttack	Hindi	MANTHRA	3m 09s	1
Cuttack	Odia	JOUTHI TU SEITHI MU	2m 56s	6
Delhi	Hindi	MISUSE	27m 39s	3
Delhi	Hindi	FADFADAA	25m 38s	21
Delhi	Bhojpuri	DHOOM MACHAIELA RAJAJEE	15m 55s	19
Delhi	Odia	SATYAM	15m 54s	1
Delhi	Hindi	AAG AUR TEZAAB	15m 41s	9
Delhi	Hindi	AAKHIRI RAAT	14m 42s	25
Delhi	Hindi	TOPLESS	14m 08s	17
Delhi	Hindi	MAIN HOON SHERNI	11m 51s	17
Delhi	Punjabi	PAUNE 9	11m 34s	14
Delhi	Hindi	FUNTASIYAN	11m 00s	2
Guwahati	Hindi	CAPITAL	3m 00s	5
Guwahati	Bodo	BEKAR ROMEO	1m 38s	4
Guwahati	Hindi Dubbed	BARUN RAI AND THE HOUSE ON THE CLIFF	1m 34s	10
Guwahati	Assamese	RAKSHAK - THE SAVIOUR	1m 08s	3
Guwahati	Assamese	BAD BOYS	0m 50s	2
Guwahati	Assamese	RONGATAPU 1982	0m 32s	3
Guwahati	Hindi	KOOKI	0m 24s	5
Guwahati	Khasi	KYNJAH	0m 23s	6
Guwahati	Manipuri	NGAMNABA LANFAMSE	0m 22s	1
Guwahati	Assamese	THE SLAM BOOK	0m 17s	4
Hyderabad	Telugu	JUNIORS	24m 00s	46
Hyderabad	Telugu	ALLUDA MAJAAKAA	20m 18s	28
Hyderabad	Telugu	1948 AKHANDA BHARATH	16m 11s	18
Hyderabad	Telugu	"NARAKASURA"	15m 55s	10
Hyderabad	Telugu	NANI	15m 50s	25
Hyderabad	Malayalam	SIMHA MUGHAM	14m 25s	18
Hyderabad	Telugu	LAAL SALAAM	14m 15s	25
Hyderabad	Telugu	"NAYEEM DIARIES" (A TO UA)	13m 28s	25
Hyderabad	Telugu	VIKKI DADA	13m 07s	22
Hyderabad	Telugu	"OREY RIKSHAW" (A TO UA)	12m 54s	24
Kolkata	Bengali	MAAHIYA (REVISED)	13m 23s	11
Kolkata	Hindi	PYAR SE PYAR TAK (REVISED)	9m 02s	7
Kolkata	Bengali	CIRCLE (REVISED)	8m 34s	18
Kolkata	Bengali	ROMANTIC NOY ( REVISED)	7m 27s	31
Kolkata	Bengali	MAHANAYAK UTTAM KUMAR ( THE METRO STATION ) ( REVISED)	6m 34s	5
Kolkata	Bengali	CHOCOLATE(REVISED)	6m 24s	22
Kolkata	Bengali	ANGAAR (REVISED)	6m 02s	33
Kolkata	Bengali	BIJOYA DASHAMI (REVISED)	5m 50s	18
Kolkata	Bengali	SURJO THE BOSS	5m 48s	3
Kolkata	Bengali	VIRUS-DEHER NOI...MONER ( REVISED)	4m 26s	15
Mumbai	English	VINCENT N ROXXY	29m 26s	28
Mumbai	English	BULLETPROOF 2	28m 02s	33
Mumbai	Marathi	NAY VARAN BHAAT LONCHA KON NAY KONCHA(REVISED)	25m 06s	17
Mumbai	Bhojpuri	JIDDI ASHIQUE	24m 47s	27
Mumbai	Bhojpuri	DAROGA BABUNI	24m 25s	25
Mumbai	Hindi	AKSAR - 2	23m 52s	7
Mumbai	Hindi	OMG 2	23m 33s	25
Mumbai	Portugal	CITY OF GOD	22m 47s	68
Mumbai	Hindi	BHAIRAVA GEETHA	21m 19s	40
Mumbai	Bhojpuri	BAA KEHU MAAI KE LAAL	21m 09s	7
Thiruvananthpuram	Malayalam	THANKAMANI	25m 25s	6
Thiruvananthpuram	Malayalam	ATTENTION PLEASE	22m 28s	3
Thiruvananthpuram	Malayalam	VIRAL 2020	22m 24s	2
Thiruvananthpuram	Malayalam	UDAL	21m 39s	69
Thiruvananthpuram	Malayalam	ARTHARAATHRI PANTHRANDU MUTHAL AARU VARE	21m 32s	3
Thiruvananthpuram	Malayalam	KARIMPULI (RE-REVISED)	20m 53s	15
Thiruvananthpuram	Malayalam	ARDHARAATHRI (REVISED)	20m 19s	18
Thiruvananthpuram	Malayalam	L	20m 06s	4
Thiruvananthpuram	Malayalam	BOOMERANG	19m 52s	2
Thiruvananthpuram	Malayalam	PRANAMAM	19m 19s	20

# Export top censored films data with slug included
write_json(top_censored_by_office, "top_censored_films.json", pretty = TRUE, auto_unbox = TRUE)

1.11 Further Information

For more details, interactive charts, and the full explorer, please visit our project website: https://cbfc.watch

The analysis was conducted by Aman Bhargava and Vivek Matthew for Diagram Chasing.

1.11.1 How to Cite

Bhargava, A., Matthew, V., & Diagram Chasing. (2025). Analyzing Film Censorship in India: CBFC Watch. Retrieved from https://cbfc.watch.

BibTeX Entry

For use in LaTeX documents, you can use the following BibTeX entry:

@misc{Bhargava2025CBFC,
  author = {Bhargava, Aman and Matthew, Vivek and {Diagram Chasing}},
  title  = {Analyzing Film Censorship in India: CBFC Watch},
  year   = {2025},
  month  = {September},
  howpublished = {\url{https://cbfc.watch}},
  note   = {Analysis and visualizations by Diagram Chasing. Last accessed: \today}
}